Professor Yu-Chi Wu was born in Kaohsiung, Taiwan. He received the Electrical Engineering Diploma in 1984 from the National Kaohsiung Institute of Technology, Taiwan, the M.S. and Ph. D. degrees in Electrical and Computer Engineering both in 1993 from the Georgia Institute of Technology (Georgia Tech), U.S.A. He has been involved in activities in both academic and industrial areas since 1986. His industrial work experience with Pacific Gas and Electric Company (PG&E) from 1989 to 1990, GM/EDS/Energy Management Associates (EMA) from 1992 to 1993, and GM/EDS/China Management Systems (CMS) from 1994 to 1995 includes development and study of Energy Management System (EMS) applications and power system planning. He joined Lien-Ho Institute of Technology and Commerce (later renamed as National United University), Taiwan in 1994 and has served in different capacities since then. He was Chairman of Department of Electrical Engineering from 1995 to 1998, Director of Library from 2000 to 2001 and from 2016~2018, Dean of Student Affairs from 2003 to 2006, Dean of College of Electrical Engineering and Computer Science from 2012~2015, Vice President from 2016~2017, National United University. He was a visiting scholar at the Alexandria Research Institute, Virginia Polytechnic Institute and State University, U.S.A. (Nov. 2002~March 2003) and University of New South Wales, Australia, Australia (July~Nov. 2010). Currently, he is a professor of Department of Electrical Engineering in the National United University.
Professor Wu’s research interest is the areas of operations of power system and microgrid, optimization algorithms, AI, design of wind generator, design of Pulley motor, mobile health management, IOT, and energy saving technology. He has published over 100 papers in the above areas. He has also won several research and invention awards: Class A Research Awards of National Science Council of Taiwan, Class A Research Awards of Lien-Ho Education Foundation of Technology and Commerce, Distinguished Alumni Awards of National Kaohsiung University of Applied Sciences, First Prize of Energy Saving Contest of Schneider Electrical Engineering Cup, Outstanding Specialist Awards of National Science Council of Taiwan, Gold Medal and Bronze Medals of Taipei International Invention show and Technomart, Bronze Medals of Le Concours Lepine, and Best Paper Awards of various international conferences. Professor Wu is a senior member of IEEE.
Smart System Lab is a lab run by several professor in the National United University who are working with several different departments (electrical engineering, computer science and information engineering, and information management). The projects conducted by this Lab are related to the areas in machine learnings, wearable devices for healthcare, smart grid, smart manufacturing, power system operations, etc.
Machine learning for IMU denoising and error reduction, Wearable devices for motion capture, Remaining useful life for switch mode power supplies, Power system and microgrid operations, Optimization algorithms, Mobile health management, IOT, and Energy saving technology.
He has published over 100 papers in the above areas. He has also won several research and invention awards: Class A Research Awards of National Science Council of Taiwan, Class A Research Awards of Lien-Ho Education Foundation of Technology and Commerce, Distinguished Alumni Awards of National Kaohsiung University of Applied Sciences, First Prize of Energy Saving Contest of Schneider Electrical Engineering Cup, Outstanding Specialist Awards of National Science Council of Taiwan, Gold Medal and Bronze Medals of Taipei International Invention Show and Technomart, Bronze Medals of Le Concours Lepine, and Best Paper Awards of various international conferences. Professor Wu is a senior member of IEEE and currently serves as the chairman of PE-31 of the IEEE Taipei Chapter.
Professor Yu-Chi Wu received the Electrical Engineering Diploma in 1984 from the National Kaohsiung Institute of Technology, Taiwan, the M.S. and Ph. D. degrees in Electrical and Computer Engineering both in 1993 from the Georgia Institute of Technology (Georgia Tech), U.S.A.
1 Vacancy
Job Description
A DC/DC switching power supply is widely used in industrial systems for automatic control processes and in aircraft/vehicles/ships for instrumentation, communication, navigation radar, etc. It performs many critical functions, and in case of failure, the working status of the equipment connected to it will be
adversely affected, which may lead to control errors or control failures for the process control system. Therefore, it is necessary to study the real-time prediction of performance degradation and worthing development because there is a wide market demand for DC/DC power supplies operating in many industrial systems. In this project, we will develop an algorithm for real-time prediction of performance degradation for this research topic by combining entropy-empirical mode decomposition-bidirectional long and short-term memory. In addition, we will build a highly accelerated life test (HALT) environment, design the experimental procedures, and develop an automated parameter acquisition system in the project. The implementation of this project has industrial and academic value. It can solve the problem of DC/DC power supply deterioration assessment, which is widely used in industrial systems, vehicles, ships, aircraft, and other industries and can be used for early replacement or repair of suspected power supplies, which can help to avoid losses caused by deterioration failures.
Preferred Intern Education Level
Graduate student or senior undergraduate student
Skill sets or Qualities
Computer programming: Python, Matlab/Simulink.
Knowledge in Machine Learning, Power Electronics
1 Vacancy
Job Description
In practical applications, IMU data noise is generated due to the manufacturing process and surrounding environment. Additionally, when there are measurement errors, the two mathematical integrals required to calculate the position and speed of a moving target will drift over time. Therefore, to popularize low-cost IMUs for motion capture, it is necessary to accurately estimate or eliminate IMU errors.
Preferred Intern Education Level
graduate student or senior undergraduate student
Skill sets or Qualities
Python, AI/Machine Learning, or Matlab/Simulink